In accordance with some aspects of the present disclosure, an apparatus is disclosed. In some embodiments, the apparatus includes a processor and a memory. In some embodiments, the memory includes programmed instructions that, when executed by the processor, cause the apparatus to receive a request from a client; determine family of metrics; schedule the request based on the family of metrics; and in response to satisfying one or more scheduling criteria, send the request to a backend server.
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2. The apparatus of claim 1, wherein the family of metrics includes a cumulative backend utilization time.
The apparatus relates to performance monitoring in computing systems, specifically addressing the challenge of tracking and analyzing resource utilization over time. The invention provides a system that measures and evaluates backend utilization, particularly focusing on cumulative backend utilization time as part of a broader family of metrics. This metric quantifies the total time a backend system remains active or engaged in processing tasks, offering insights into efficiency, bottlenecks, and overall system health. The apparatus collects and processes this data to enable real-time or historical analysis, helping administrators optimize resource allocation and identify performance degradation. By incorporating cumulative backend utilization time alongside other metrics, the system provides a comprehensive view of backend operations, facilitating proactive management and troubleshooting. The invention is particularly useful in environments where backend performance directly impacts user experience, such as cloud computing, data centers, or distributed systems. The apparatus may include sensors, data processors, and visualization tools to present the metrics in an actionable format, ensuring timely decision-making.
3. The apparatus of claim 1, wherein the family of metrics includes a cumulative backend queue time.
The apparatus is designed for managing and optimizing data processing in a distributed computing environment, particularly focusing on improving efficiency and reducing latency in task execution. The system addresses the challenge of monitoring and optimizing backend processing times, which are critical for maintaining performance in large-scale data systems. The apparatus includes a monitoring module that tracks various performance metrics related to task execution, such as processing times, queue lengths, and resource utilization. One key metric in this family of metrics is the cumulative backend queue time, which measures the total time tasks spend waiting in the backend queue before execution. This metric helps identify bottlenecks and inefficiencies in the system, allowing for better resource allocation and task scheduling. The apparatus may also include an optimization module that uses these metrics to dynamically adjust system parameters, such as task priorities, resource allocation, or scheduling policies, to minimize backend queue times and improve overall system performance. By continuously monitoring and optimizing these metrics, the apparatus ensures efficient task processing and reduces latency in data workflows.
4. The apparatus of claim 1, wherein the sum values more recent backend times more substantially than less recent backend times.
The invention relates to a data processing apparatus designed to analyze and prioritize backend processing times in a computing system. The apparatus is configured to generate sum values representing aggregated backend processing times, where more recent backend times are given greater weight in the summation process compared to less recent backend times. This weighting mechanism ensures that the most current backend performance data has a stronger influence on the resulting sum values, allowing the system to dynamically adapt to changing conditions. The apparatus may include components for collecting backend time data, applying time-based weighting factors, and computing the weighted sum values. The invention addresses the problem of accurately reflecting current system performance by emphasizing recent data, which is particularly useful in environments where backend processing times fluctuate due to varying workloads or system conditions. By prioritizing recent backend times, the apparatus provides a more responsive and adaptive approach to performance monitoring and optimization.
7. The apparatus of claim 1, wherein scheduling includes assigning a delay, and wherein satisfying one or more scheduling criteria includes the delay elapsing.
This invention relates to a system for managing data transmission in a network environment, particularly addressing the challenge of efficiently scheduling data transfers while minimizing latency and resource contention. The apparatus includes a scheduler that dynamically assigns delays to data transmission tasks based on network conditions and system priorities. The scheduler ensures that tasks are executed only after a predefined delay has elapsed, which helps in optimizing bandwidth usage and reducing collisions in shared network resources. The system monitors network traffic and adjusts scheduling parameters in real-time to meet performance criteria, such as minimizing delay or maximizing throughput. By incorporating delay-based scheduling, the apparatus ensures that data transmissions are synchronized with network availability, improving overall efficiency and reliability. The invention is particularly useful in environments where multiple devices compete for network access, such as wireless communication systems or cloud computing platforms. The delay mechanism allows the system to prioritize critical tasks while deferring less urgent transmissions, thereby balancing load and maintaining stable network performance. The apparatus may also include mechanisms to dynamically adjust the delay duration based on changing network conditions or user-defined policies, ensuring adaptability to varying operational demands.
8. The apparatus of claim 7, wherein satisfying one or more scheduling criteria includes a rescheduling counter meeting or exceeding a rescheduling counter threshold when the delay elapses.
The invention relates to a system for managing task scheduling in a computing environment, particularly addressing the problem of inefficient task rescheduling that can lead to resource underutilization or delays. The system includes a processor and a memory storing instructions that, when executed, perform operations to monitor and adjust task scheduling based on predefined criteria. A key aspect is the use of a rescheduling counter that tracks the number of times a task has been rescheduled. When a delay occurs in task execution, the system evaluates whether the rescheduling counter meets or exceeds a predefined threshold. If the threshold is met, the system triggers a rescheduling event to optimize task execution, ensuring better resource utilization and reducing delays. The system may also include a delay timer that measures the elapsed time since the last rescheduling attempt, further refining the decision-making process. This approach helps prevent excessive rescheduling while ensuring tasks are executed efficiently. The invention is particularly useful in environments where tasks must be dynamically managed to avoid bottlenecks and improve overall system performance.
9. The apparatus of claim 1, wherein satisfying one or more scheduling criteria includes determining that a buffer is out of space between an assigned location of the buffer and an end of the buffer.
This invention relates to data processing systems, specifically buffer management in computing devices. The problem addressed is efficient buffer utilization to prevent data loss or processing delays when a buffer reaches capacity. The apparatus includes a buffer with an assigned location for storing data and a mechanism to monitor buffer space. The invention determines whether one or more scheduling criteria are met, including detecting when the buffer is out of space between its assigned location and the end of the buffer. This ensures data is properly managed before the buffer overflows, maintaining system stability. The apparatus may also include a processor to execute instructions for buffer monitoring and a memory to store buffer-related data. The system dynamically adjusts data handling based on buffer conditions, optimizing performance and preventing errors. The invention is particularly useful in real-time systems where buffer overflows could disrupt operations.
11. The medium of claim 10, wherein the family of metrics includes a cumulative backend utilization time.
A system and method for monitoring and analyzing backend utilization in a computing environment. The technology addresses the challenge of efficiently tracking and optimizing resource usage in distributed systems, where backend components may experience varying levels of demand and performance bottlenecks. The invention provides a method for calculating and storing a family of metrics related to backend utilization, including a cumulative backend utilization time. This metric represents the total time a backend system is actively processing requests or performing computational tasks over a specified period. The system collects performance data from backend components, processes this data to derive utilization metrics, and stores the results in a structured format for analysis. The cumulative backend utilization time allows administrators to assess overall backend workload, identify periods of high or low utilization, and make informed decisions about resource allocation and scaling. The invention may also include additional metrics such as request latency, throughput, and error rates to provide a comprehensive view of backend performance. By analyzing these metrics, users can detect inefficiencies, predict capacity needs, and optimize system performance. The system is designed to operate in real-time or near-real-time, enabling proactive management of backend resources.
12. The medium of claim 10, wherein the family of metrics includes a cumulative backend queue time.
A system and method for monitoring and analyzing backend queue performance in computing environments. The technology addresses inefficiencies in tracking and optimizing backend processing delays, which can lead to degraded system performance and user experience. The invention provides a family of metrics for evaluating backend queue operations, including a cumulative backend queue time metric that aggregates the total time requests spend in a backend queue over a specified period. This metric helps identify bottlenecks and inefficiencies in backend processing pipelines. The system collects and processes queue time data from multiple backend components, allowing for comprehensive performance analysis. The cumulative backend queue time metric is calculated by summing individual queue times for each request or operation, providing a holistic view of backend processing delays. The system may also include additional metrics such as average queue time, maximum queue time, and queue depth to further assess backend performance. The collected metrics are used to generate reports, alerts, or automated adjustments to optimize backend processing efficiency. The invention is particularly useful in distributed computing environments, cloud services, and high-throughput systems where backend queue management is critical for maintaining performance and reliability.
13. The medium of claim 10, wherein the sum values more recent backend times more substantially than less recent backend times.
A system and method for processing data in a distributed computing environment addresses the challenge of efficiently aggregating and analyzing time-series data from multiple backend systems. The invention involves collecting data from various backend sources, where each data point is associated with a timestamp indicating when it was generated. The system computes sum values of the data points, but applies a weighting scheme that prioritizes more recent backend times over less recent ones. This ensures that newer data has a greater influence on the aggregated results, improving the accuracy and relevance of time-sensitive analyses. The method includes steps for receiving data from backend systems, assigning timestamps to the data, and applying a time-based weighting function to the sum values. The weighting function dynamically adjusts the contribution of each data point based on its recency, allowing the system to adapt to real-time changes in the backend data. This approach is particularly useful in applications where timely insights are critical, such as financial monitoring, network performance analysis, or real-time decision-making systems. The invention enhances the reliability of aggregated data by reducing the impact of outdated information, leading to more accurate and actionable insights.
17. The method of claim 16, wherein the family of metrics includes a cumulative backend utilization time.
A system and method for monitoring and optimizing backend resource utilization in computing environments. The invention addresses inefficiencies in tracking backend performance, particularly in distributed systems where resource allocation and utilization are dynamic and often opaque. Traditional monitoring tools lack granular metrics that correlate backend activity with overall system performance, leading to suboptimal resource management and potential bottlenecks. The method involves collecting and analyzing a family of metrics related to backend operations. A key metric is cumulative backend utilization time, which measures the total time backend resources are actively processing tasks over a defined period. This metric helps quantify backend workload distribution, identify underutilized or overburdened resources, and optimize task scheduling. The system may also track other metrics such as response times, error rates, and concurrency levels to provide a comprehensive view of backend performance. By aggregating these metrics, the system enables real-time adjustments to resource allocation, load balancing, and task prioritization. The cumulative backend utilization time metric is particularly useful for detecting long-term trends in resource usage, allowing for proactive scaling and capacity planning. The method can be applied to various backend architectures, including cloud-based services, microservices, and traditional server farms, to improve efficiency and reliability.
18. The method of claim 16, wherein the family of metrics includes a cumulative backend queue time.
A system and method for monitoring and optimizing backend processing in a computing environment addresses inefficiencies in tracking and managing workload delays. The invention provides a framework for evaluating backend processing performance using a family of metrics, including a cumulative backend queue time. This metric quantifies the total time workloads spend in a queue before being processed, offering insights into system bottlenecks and delays. The method involves collecting data on queue times across multiple processing stages, aggregating these times to compute the cumulative metric, and using this information to identify inefficiencies, optimize resource allocation, and improve overall system performance. By analyzing cumulative backend queue time alongside other metrics, the system enables proactive management of workload delays, reducing latency and enhancing processing efficiency. The approach is particularly useful in high-throughput environments where minimizing queue times is critical for maintaining performance and responsiveness. The invention may be applied in cloud computing, data centers, or any system where backend processing delays impact user experience or operational efficiency.
19. The method of claim 16, wherein the sum values more recent backend times more substantially than less recent backend times.
A system and method for processing data in a distributed computing environment addresses the challenge of efficiently aggregating and analyzing time-series data across multiple backend systems. The method involves collecting data from various backend systems, where each backend system generates data with associated timestamps. The collected data is aggregated into sum values, which represent the total of data points over specific time intervals. The method then applies a weighting scheme to these sum values, where more recent backend times are given greater importance than less recent backend times. This ensures that the most current data has a stronger influence on the final analysis, improving the accuracy and relevance of the results. The system may also include mechanisms for handling data inconsistencies, such as missing or corrupted data points, to maintain reliability. The method is particularly useful in applications requiring real-time or near-real-time data processing, such as financial analytics, network monitoring, or industrial control systems. By prioritizing recent data, the system provides a more responsive and adaptive approach to data analysis, enhancing decision-making processes.
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September 11, 2023
June 4, 2024
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